基于互联网金融的投资者风险感知、信息不对称与尾部市场效率研究

Fintech industries have been booming recently, such as microfinance, crowd-funding, and peer-to-peer lending, which opens new channels for investors and borrowers. However, there are a series of severe problems happened in Fintech industries. The risk has been accumulated in this area and thus, resources are not allocated in a reasonable way. Fintech technologies improve information broadcasting between investors and borrowers; however, we argue that Fintech doesn’t decrease information asymmetry. Therefore, this project is aimed to improve long tail market efficiency based on information asymmetry theory. .Firstly, in P2P and crowd-funding industries, most borrowers and investors are individual persons, which mean they may show more behavioral characteristics in transaction. Based on big data techniques, we hence conduct extensive tests of the factors driving investors in choosing products in the Chinese Fintech ecosystem, and then compare these preferences to those of investors in traditional financial institutions and products. We will also investigate the effects of funder’s heterogeneity on transactional outcomes and behavior patterns by using “the cumulative link model with logit” and other basic cross-sectional and panel regression analytical methods. We also use Markov regime switching model to test whether the risk preference will change or not when investors have more investing experience. .Secondly, Fintech assists capital resource allocating and reduces the transaction cost, but does it reduce information asymmetry indeed, although it collects and shares information about investors and borrowers? What factor is crucial in improving market efficiency? The price discovery process helps incorporate all the available information in the market into the prices of instruments is a measure of a market’s efficiency. Based on this theory, we try to construct a model to explain how the Fintech can benefit the long tail market. .Lastly, we try to explain the difference between information symmetry and information connection and their roles in allocating resources. We then incorporate information asymmetry into the model and explain how to minimize risk in price discovery process. .This study can push investors more reasonable in the market and thus improves the efficiency of the market.

互联网金融被认为是提高尾部市场效率的创新模式，然而实业界中的种种问题表明，现阶段的互联网金融堆积了高度的风险，没有实现资源的有效配置。本项目抓住“信息不对称”这一要素，提出在有限理性投资者市场中，信息对称是保障投资者作出最优价值判断、提高价格发现质量、提升市场效率的关键。.首先，以个体为主的尾部市场参与者体现出多元化的特点，利用深度学习方法的“卷积链接模型”，探测投资主体的有限理性行为，挖掘投资者风险感知和风险偏好转移规律。其次，通过区分“信息对称”与“信息对接”在实现资源配置过程中的功能差异，剖析阻碍尾部市场效率提升的要素壁垒，并刻画“信息不对称”要素的风险特征。再次，建立引入“信息不对称”要素的尾部市场效率模型，解决金融资源的合理配置问题。通过本项目的研究，有助于提高投资者风险感知能力，促进参与主体理性回归，降低“信息不对称”带来的风险价值，真正实现提高尾部市场效率的目标。

本项目通过采集多种类行业的互联网金融投资行为信息，提炼互联网金融领域投资者有限理性行为特征，对比传统行业，总结分析互联网金融行业投资者过度风险偏好特征；剖析互联网金融创新模式与尾部市场投资者需求，总结提炼行业监管模式与监管特征，以演化博弈模型剖析了投资者偏好、监管导向对互联网金融行业创新模式的影响，由静态向动态深入提炼最优市场结构与资源配置状态；刻画互联网金融中的信息不对称性及其风险特征，按照行业类型，分别构建风险特征衡量方法与监测指标，多维度、多指标综合度量风险状态与预警线，将科研应用于实践，为互联网金融行业风险监管和政策制定提供理论支撑。.(1)目前已有13篇文章正式发表在包括International Review of Economics and Finance(SSCI)、Applied Economics (SSCI) 等国内外期刊和国际会议论文集中。其中，SCI/SSCI期刊索引8篇（累计影响因子13.463）、国家自然科学基金委管理学部重要期刊2篇、EI会议索引3篇。.(2)培养博士生6人（其中已毕业2人、提交预审论文1人），硕士毕业生8人。.(3)在中国信息经济学年会连续4年举办金融科技学术论坛、在全欧中国经济学年会（CEA）、中国金融系统工程与风险管理国际年会等会议上主持以金融科技为主题的分论坛（9次）。.(4)分别建立了“P2P行业企业信息库”、“互联网股权融资企业信息数据库”、“互联网保险企业信息数据库”。随着互联网金融整体行业出现“风险积聚”的现实发展进程，进而分别构建了风险监测指标体系，包括P2P平台的风险监测指标体系（6个维度、50项指标）、互联网股权融资平台的风险监测指标体系（4个维度、39项指标）、互联网保险平台的风险监测指标体系（4个维度、37项指标）。并与国家计算机网络与信息安全管理中心共同合作，推动监管科技在互联网金融领域的实践应用。